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Energies
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Machine Learning and Complex Networks Analysis
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Machine Learning and Complex Networks Analysis

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 4135

Special Issue Editors

Prof. Dr. Giuliano Armano

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: classification and clustering methods; classifier ensembles; hierarchical classification; methods for assessing feature importance; phase transitions in complex networks; social networks dynamics
Dr. Paolo Attilio Pegoraro

E-Mail Website
Co-Guest Editor
Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Interests: new measurement techniques; measurement uncertainty and propagation analysis; measurements for modern power networks; synchronized instruments; distributed measurement systems; power system state estimation; compressive sensing methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As Guest Editors, we are pleased to invite you to submit manuscripts to a Special Issue of Energies on the subject area of “Machine Learning and Complex Networks Analysis”.

Machine learning and complex networks are increasingly popular and pervasive approaches, which have demonstrated their validity across multiple research and application fields—to the point that many of these fields have received a further boost thanks to them.

This Special Issue is focused on the application of ML techniques and CN analysis to methods, systems, applications, and research related to energy, exergy and energetics.

Topics of interest for publication include, but are not limited to, the use of ML and CNs to the following application fields:

  • Power system control and optimization
  • Optimal power flow analysis
  • Power system state estimation
  • Adaptive behaviour of energy systems
  • Energy demand management, storage and distribution
  • Distributed energy resources and smart grids
  • Advanced metering infrastructures
  • Energy conversion, saving and efficiency
  • Energy markets and analysis of energy distribution time series
  • Energy and environmental indicators
  • Exergy analysis and environmental equilibrium
  • Renewable energy, energetics and environmental science

Submit your paper and select the Journal “Energies” and the Special Issue “Machine Learning and Complex Networks Analysis” via: MDPI submission system. Please contact the special issue editor ([email protected]) for any queries. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Prof. Dr. Giuliano Armano
Guest Editor

Prof. Dr. Paolo Attilio Pegoraro
co-Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial neural networks/deep learning
  • ML monolithic/ensemble methods
  • ML performance measures
  • Clustering techniques
  • Scale-free/small-world networks
  • Spatial networks/spatial modular networks

Published Papers (2 papers)

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Research

15 pages, 5499 KiB  
Article
Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads
by Giuliano Armano and Paolo Attilio Pegoraro
Energies 2022, 15(2), 549; https://0-doi-org.brum.beds.ac.uk/10.3390/en15020549 - 13 Jan 2022
Cited by 1 | Viewed by 1101
Abstract
The design of new monitoring systems for intelligent distribution networks often requires both real-time measurements and pseudomeasurements to be processed. The former are obtained from smart meters, phasor measurement units and smart electronic devices, whereas the latter are predicted using appropriate algorithms—with the [...] Read more.
The design of new monitoring systems for intelligent distribution networks often requires both real-time measurements and pseudomeasurements to be processed. The former are obtained from smart meters, phasor measurement units and smart electronic devices, whereas the latter are predicted using appropriate algorithms—with the typical objective of forecasting the behaviour of power loads and generators. However, depending on the technique used for data encoding, the attempt at making predictions over a period of several days may trigger problems related to the high number of features. To contrast this issue, feature importance analysis becomes a tool of primary importance. This article is aimed at illustrating a technique devised to investigate the importance of features on data deemed relevant for predicting the next hour demand of aggregated, medium-voltage electrical loads. The same technique allows us to inspect the hidden layers of multilayer perceptrons entrusted with making the predictions, since, ultimately, the content of any hidden layer can be seen as an alternative encoding of the input data. The possibility of inspecting hidden layers can give wide support to researchers in a number of relevant tasks, including the appraisal of the generalisation capability reached by a multilayer perceptron and the identification of neurons not relevant for the prediction task. Full article
(This article belongs to the Special Issue Machine Learning and Complex Networks Analysis)
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13 pages, 1125 KiB  
Article
Twin-Delayed Deep Deterministic Policy Gradient for Low-Frequency Oscillation Damping Control
by Qiushi Cui, Gyoungjae Kim and Yang Weng
Energies 2021, 14(20), 6695; https://0-doi-org.brum.beds.ac.uk/10.3390/en14206695 - 15 Oct 2021
Cited by 6 | Viewed by 2425
Abstract
Due to the large scale of power systems, latency uncertainty in communications can cause severe problems in wide-area measurement systems. To resolve this issue, a significant amount of past work focuses on using emerging technology, including machine learning methods such as Q-learning, for [...] Read more.
Due to the large scale of power systems, latency uncertainty in communications can cause severe problems in wide-area measurement systems. To resolve this issue, a significant amount of past work focuses on using emerging technology, including machine learning methods such as Q-learning, for addressing latency issues in modern controls. Although the method can deal with the stochastic characteristics of communication latency, the Q-values can be overestimated in Q-learning methods, leading to high bias. To address the overestimation bias issue, we redesign the learning structure of the deep deterministic policy gradient (DDPG). Then we develop a damping control twin-delayed deep deterministic policy gradient method to handle the damping control issue under unknown latency in the power network. The purpose is to address the damping control issue under unknown latency in the power network. This paper will create a novel reward algorithm, taking into account the machine speed deviation, the episode termination prevention, and the feedback from action space. In this way, the system optimally damps down frequency oscillations while maintaining the system’s stability and reliable operation within defined limits. The simulation results verify the proposed algorithm in various perspectives, including the latency sensitivity analysis under high renewable energy penetration and the comparison with conventional and machine learning control algorithms. The proposed method shows a fast learning curve and good control performance under varying communication latency. Full article
(This article belongs to the Special Issue Machine Learning and Complex Networks Analysis)
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